Social media, e-commerce, “Big Data,” and other modern computing paradigms and applications may access, store, and use information entered online by users through blogs, personal Web pages, online forums, social-media outlets, and other online venues. Organizations that leverage data from an internal source, such as a call center, a tech-support operation, a customer-management or CRM function, or a marketing or sales department, could benefit from augmenting this internally generated data with ancillary data captured or inferred from public or private, user-generated or commercial, online sources. Such an effort may, however, be hampered by inefficiencies and constraints that include:                security issues that arise when interfacing with, or when importing information from, ad hoc online sources, or when attempting to enhance user data retrieved from such a source by allowing that source to associate user-entered data with personally identifying information;        an inability to identify or fully characterize individuals who choose not to disclose their identities in a public form;        a likelihood of omitting important information that was posted in a lower-priority or lower-popularity online venue;        a difficulty of inferring meaning and patterns from, and in recognizing relationships among, user entries that are received without context; or        an inability to provide a customized experience to users who desire special treatment based on a personal characteristic, such as a preferred status, a medical condition, or a dietary requirement.        
There is thus a need for a way to allow a user to identify itself or a set of its special needs in a public or privately managed venue or data repository, such that: i) the entered data may be enriched with context and inferences that allow an application to give the user a customized experience; and ii) to do so without compromising the user's personal security or privacy.